Evaluation of radioanatomical and biochemical features of patients with hyperlipidaemia by conventional angiography
Öz
Purpose: The aim of this study is to evaluate the cardiovascular and biochemical changes caused by hyperlipidemia using machine learning (ML) algorithms.
Materials and Methods: This retrospective study was performed on conventional coronary angiography images and biochemical findings of 70 patients with hyperlipidemia (Group 1) and 150 individuals without significant coronary artery stenosis (Group 2). Conventional angiography images were retrospectively reviewed, and diameter measurements of the proximal, middle, crux, distal, posterolateral, and posterior descending branches of the right coronary artery were obtained from right anterior oblique cranial projections. The diameters of the left main coronary artery, the proximal anterior interventricular artery, and the proximal circumflex artery were measured from left anterior oblique caudal projections.
Results: ML algorithms predicted hyperlipidemia with an accuracy of 0.89-0.98. SHAP analysis identified the initial diameter of the left main coronary artery as the most influential radioanatomical feature and triglyceride level as the most influential biochemical feature contributing to the machine learning model predictions.
Conclusions: High rates of hyperlipidemia disease were predicted using radioanatomical and biochemical findings. We believe that these results will contribute to the early diagnosis and early treatment protocol of hyperlipidemia and other related diseases.
Anahtar Kelimeler
Hyperlipidemia, conventional angiography, machine learning algorithms, radioanatomical markers, biochemical markers.
Etik Beyan
The study was approved by the decision of Karabük University Non-Interventional Ethics Committee on 05.11.2024 with the number 2024/1974.
Teşekkür
I would like to thank everyone who has contributed to science.
Kaynakça
- Dritsas E, Trigka M. Machine learning methods for hypercholesterolemia long-term risk prediction. Sensors. 2022;22:5365.
- Kris-Etherton PM, Sanders L, Lawler O, Riley T, Maki K. Hyperlipidemia. In Encyclopedia of Human Nutrition. 4th ed. New York, Elsevier; 2023;361-79.
- Pham HN, Ibrahim R, Sainbayar E et al. Burden of hyperlipidemia, cardiovascular mortality, and COVID‐19: a retrospective‐cohort analysis of US data. J Am Heart Assoc. 2025:e037381.
- Biccirè FG, Gatto L, La Porta Y, Pignatelli P, Prati F, Pastori D. Effects of lipid lowering therapies on vulnerable plaque features: an updated narrative review of the literature. J Cardiovasc Dev Dis. 2023;10:260.
- Vincent MJ, Allen B, Palacios OM, Haber LT, Maki KC. Meta-regression analysis of the effects of dietary cholesterol intake on LDL and HDL cholesterol. Am J Clin Nutr. 2019;109:7-16.
- Nelson RH. Hyperlipidemia as a risk factor for cardiovascular disease. Prim Care. 2012;40:195-211.
- Ross R. Atherosclerosis an inflammatory disease. N Engl J Med. 1999;340:115-26.
- Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105:1135-43.
- Gimbrone Jr MA, García-Cardeña G. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circ Res. 2016;118:620-36.
- Glagov S, Weisenberg E, Zarins CK, Stankunavicius R, Kolettis GJ. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med. 1987;316:1371-75.
